Crowdsourcing and Multisource Fusion-Based Fingerprint Sensing in Smartphone Localization

Traditional WiFi fingerprint positioning normally uses a time consuming and labor intensive process called site survey. This paper proposes a crowdsourcing and multisource fusion-based fingerprint sensing (CMFS) to replace the traditional site survey approach. In CMFS, motion sensors are used to construct the radio map by volunteers and pedestrian dead reckoning based on motion sensors equipped in smartphones is used for positioning. In positioning phase, an extended Kalman filter (EKF)-based multisource fusion algorithm is further developed to tackle the nonlinear fusion so that both positioning accuracy and robustness can be improved. Both experimental and simulation results verify that the performance of proposed schemes is comparable to the traditional fingerprint approaches. Further the EKF-based fusion scheme is found to improve smoothness and stability of CMFS greatly.